Adaptive Artificial Intelligence for Dynamic Environments: Developing Systems That Learn from Changing Conditions

Deshen Moodley

Adaptive Artificial Intelligence for Dynamic Environments: Developing Systems That Learn from Changing Conditions

Author(s)	Deshen Moodley
Country	United States
Abstract	Artificial intelligence systems are commonly trained under the assumption that the statistical properties of their operating environment will remain sufficiently stable after deployment. This assumption is unsuitable for applications in which user behavior, sensor conditions, operational constraints, threat patterns, or decision objectives change over time. The present simulation-based study examines an adaptive artificial intelligence architecture designed to identify environmental change, update its internal representation, retain relevant prior knowledge, and regulate the extent of model adaptation. The proposed architecture integrates online learning, concept-drift detection, uncertainty estimation, experience replay, and adaptive update control. Its performance was evaluated through a transparent simulated classification environment containing gradual, abrupt, recurring, and mixed distribution shifts. Four model configurations were compared: a static baseline, periodic retraining, unrestricted online learning, and the proposed adaptive system. The simulated results indicate that the adaptive configuration maintained the highest mean post-change accuracy, achieved the shortest recovery period, and produced a more favorable balance between new-condition learning and prior-knowledge retention. Its mean accuracy across all drift scenarios reached 88.05%, compared with 71.75% for the static model, 81.83% for periodic retraining, and 83.60% for unrestricted online learning. The adaptive architecture also reduced forgetting by selectively replaying representative historical observations and adjusting its learning rate in response to drift magnitude and predictive uncertainty. These findings do not constitute real-world experimental validation; rather, they offer a reproducible methodological framework and theoretically grounded evidence for evaluating adaptive intelligence under controlled nonstationarity. The study concludes that effective adaptation depends not merely on frequent model updating but on coordinated mechanisms for change detection, calibrated plasticity, memory retention, safety monitoring, and accountable human oversight.
Keywords	adaptive artificial intelligence, concept drift, continual learning, dynamic environments, online learning, catastrophic forgetting, uncertainty estimation, experience replay.
Field	Engineering
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-02

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American Journal of Advanced Multidisciplinary Research and Innovation

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Adaptive Artificial Intelligence for Dynamic Environments: Developing Systems That Learn from Changing Conditions

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